Overview

Dataset statistics

Number of variables19
Number of observations5020
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory784.4 KiB
Average record size in memory160.0 B

Variable types

Text1
Numeric10
DateTime1
Categorical7

Alerts

Price_x is highly overall correlated with Price_y and 2 other fieldsHigh correlation
Qty is highly overall correlated with TotalAmountHigh correlation
TotalAmount is highly overall correlated with QtyHigh correlation
StoreID is highly overall correlated with Latitude and 3 other fieldsHigh correlation
Latitude is highly overall correlated with StoreID and 3 other fieldsHigh correlation
Longitude is highly overall correlated with StoreName and 2 other fieldsHigh correlation
Price_y is highly overall correlated with Price_x and 2 other fieldsHigh correlation
Age is highly overall correlated with Income and 1 other fieldsHigh correlation
Income is highly overall correlated with AgeHigh correlation
ProductID is highly overall correlated with Price_x and 2 other fieldsHigh correlation
StoreName is highly overall correlated with StoreID and 4 other fieldsHigh correlation
GroupStore is highly overall correlated with StoreID and 4 other fieldsHigh correlation
Type is highly overall correlated with StoreID and 4 other fieldsHigh correlation
Product Name is highly overall correlated with Price_x and 2 other fieldsHigh correlation
Marital Status is highly overall correlated with AgeHigh correlation
Income has 185 (3.7%) zerosZeros

Reproduction

Analysis started2023-09-15 09:47:51.509929
Analysis finished2023-09-15 09:48:33.003276
Duration41.49 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Distinct4908
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:33.504309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8822709
Min length4

Characters and Unicode

Total characters34549
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4797 ?
Unique (%)95.6%

Sample

1st rowTR11369
2nd rowTR16356
3rd rowTR1984
4th rowTR35256
5th rowTR41231
ValueCountFrequency (%)
tr71313 3
 
0.1%
tr57126 2
 
< 0.1%
tr88968 2
 
< 0.1%
tr72611 2
 
< 0.1%
tr6940 2
 
< 0.1%
tr78366 2
 
< 0.1%
tr51183 2
 
< 0.1%
tr61742 2
 
< 0.1%
tr33585 2
 
< 0.1%
tr13665 2
 
< 0.1%
Other values (4898) 4999
99.6%
2023-09-15T09:48:34.609220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 5020
14.5%
R 5020
14.5%
2 2572
7.4%
4 2557
7.4%
9 2521
7.3%
7 2515
7.3%
8 2511
7.3%
3 2500
7.2%
1 2497
7.2%
6 2457
7.1%
Other values (2) 4379
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24509
70.9%
Uppercase Letter 10040
29.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2572
10.5%
4 2557
10.4%
9 2521
10.3%
7 2515
10.3%
8 2511
10.2%
3 2500
10.2%
1 2497
10.2%
6 2457
10.0%
5 2447
10.0%
0 1932
7.9%
Uppercase Letter
ValueCountFrequency (%)
T 5020
50.0%
R 5020
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24509
70.9%
Latin 10040
29.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2572
10.5%
4 2557
10.4%
9 2521
10.3%
7 2515
10.3%
8 2511
10.2%
3 2500
10.2%
1 2497
10.2%
6 2457
10.0%
5 2447
10.0%
0 1932
7.9%
Latin
ValueCountFrequency (%)
T 5020
50.0%
R 5020
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 5020
14.5%
R 5020
14.5%
2 2572
7.4%
4 2557
7.4%
9 2521
7.3%
7 2515
7.3%
8 2511
7.3%
3 2500
7.2%
1 2497
7.2%
6 2457
7.1%
Other values (2) 4379
12.7%

CustomerID
Real number (ℝ)

Distinct447
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.26375
Minimum1
Maximum447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:35.091759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q1108
median221
Q3332
95-th percentile426
Maximum447
Range446
Interquartile range (IQR)224

Descriptive statistics

Standard deviation129.67296
Coefficient of variation (CV)0.58605604
Kurtosis-1.1934547
Mean221.26375
Median Absolute Deviation (MAD)112
Skewness0.022381467
Sum1110744
Variance16815.075
MonotonicityNot monotonic
2023-09-15T09:48:35.554018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156 21
 
0.4%
365 20
 
0.4%
392 20
 
0.4%
89 19
 
0.4%
44 19
 
0.4%
245 19
 
0.4%
13 19
 
0.4%
445 18
 
0.4%
444 18
 
0.4%
189 18
 
0.4%
Other values (437) 4829
96.2%
ValueCountFrequency (%)
1 17
0.3%
2 13
0.3%
3 15
0.3%
4 10
0.2%
5 7
0.1%
6 10
0.2%
7 17
0.3%
8 14
0.3%
9 10
0.2%
10 14
0.3%
ValueCountFrequency (%)
447 13
0.3%
446 11
0.2%
445 18
0.4%
444 18
0.4%
443 16
0.3%
442 13
0.3%
441 5
 
0.1%
440 12
0.2%
439 7
 
0.1%
438 14
0.3%

Date
Date

Distinct365
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size78.4 KiB
Minimum2022-01-01 00:00:00
Maximum2022-12-31 00:00:00
2023-09-15T09:48:35.981687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:36.438966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ProductID
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.4 KiB
P5
814 
P10
620 
P2
530 
P7
522 
P3
519 
Other values (5)
2015 

Length

Max length3
Median length2
Mean length2.123506
Min length2

Characters and Unicode

Total characters10660
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP3
2nd rowP9
3rd rowP1
4th rowP1
5th rowP9

Common Values

ValueCountFrequency (%)
P5 814
16.2%
P10 620
12.4%
P2 530
10.6%
P7 522
10.4%
P3 519
10.3%
P9 488
9.7%
P8 485
9.7%
P1 397
7.9%
P4 390
7.8%
P6 255
 
5.1%

Length

2023-09-15T09:48:36.896171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-15T09:48:37.285248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
p5 814
16.2%
p10 620
12.4%
p2 530
10.6%
p7 522
10.4%
p3 519
10.3%
p9 488
9.7%
p8 485
9.7%
p1 397
7.9%
p4 390
7.8%
p6 255
 
5.1%

Most occurring characters

ValueCountFrequency (%)
P 5020
47.1%
1 1017
 
9.5%
5 814
 
7.6%
0 620
 
5.8%
2 530
 
5.0%
7 522
 
4.9%
3 519
 
4.9%
9 488
 
4.6%
8 485
 
4.5%
4 390
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5640
52.9%
Uppercase Letter 5020
47.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1017
18.0%
5 814
14.4%
0 620
11.0%
2 530
9.4%
7 522
9.3%
3 519
9.2%
9 488
8.7%
8 485
8.6%
4 390
 
6.9%
6 255
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
P 5020
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5640
52.9%
Latin 5020
47.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1017
18.0%
5 814
14.4%
0 620
11.0%
2 530
9.4%
7 522
9.3%
3 519
9.2%
9 488
8.7%
8 485
8.6%
4 390
 
6.9%
6 255
 
4.5%
Latin
ValueCountFrequency (%)
P 5020
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 5020
47.1%
1 1017
 
9.5%
5 814
 
7.6%
0 620
 
5.8%
2 530
 
5.0%
7 522
 
4.9%
3 519
 
4.9%
9 488
 
4.6%
8 485
 
4.5%
4 390
 
3.7%

Price_x
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9684.8008
Minimum3200
Maximum18000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:37.697744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3200
5-th percentile3200
Q14200
median9400
Q315000
95-th percentile18000
Maximum18000
Range14800
Interquartile range (IQR)10800

Descriptive statistics

Standard deviation4600.7088
Coefficient of variation (CV)0.47504423
Kurtosis-1.1395182
Mean9684.8008
Median Absolute Deviation (MAD)5200
Skewness0.16819672
Sum48617700
Variance21166521
MonotonicityNot monotonic
2023-09-15T09:48:38.086758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4200 814
16.2%
15000 620
12.4%
3200 530
10.6%
9400 522
10.4%
7500 519
10.3%
10000 488
9.7%
16000 485
9.7%
8800 397
7.9%
12000 390
7.8%
18000 255
 
5.1%
ValueCountFrequency (%)
3200 530
10.6%
4200 814
16.2%
7500 519
10.3%
8800 397
7.9%
9400 522
10.4%
10000 488
9.7%
12000 390
7.8%
15000 620
12.4%
16000 485
9.7%
18000 255
 
5.1%
ValueCountFrequency (%)
18000 255
 
5.1%
16000 485
9.7%
15000 620
12.4%
12000 390
7.8%
10000 488
9.7%
9400 522
10.4%
8800 397
7.9%
7500 519
10.3%
4200 814
16.2%
3200 530
10.6%

Qty
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6446215
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:38.464206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8552954
Coefficient of variation (CV)0.50905022
Kurtosis0.39205731
Mean3.6446215
Median Absolute Deviation (MAD)1
Skewness0.67510385
Sum18296
Variance3.4421209
MonotonicityNot monotonic
2023-09-15T09:48:38.825384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 1078
21.5%
2 915
18.2%
4 869
17.3%
5 802
16.0%
1 601
12.0%
6 402
 
8.0%
7 218
 
4.3%
8 52
 
1.0%
10 44
 
0.9%
9 39
 
0.8%
ValueCountFrequency (%)
1 601
12.0%
2 915
18.2%
3 1078
21.5%
4 869
17.3%
5 802
16.0%
6 402
 
8.0%
7 218
 
4.3%
8 52
 
1.0%
9 39
 
0.8%
10 44
 
0.9%
ValueCountFrequency (%)
10 44
 
0.9%
9 39
 
0.8%
8 52
 
1.0%
7 218
 
4.3%
6 402
 
8.0%
5 802
16.0%
4 869
17.3%
3 1078
21.5%
2 915
18.2%
1 601
12.0%

TotalAmount
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32279.482
Minimum7500
Maximum88000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:39.247395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7500
5-th percentile8400
Q116000
median28200
Q347000
95-th percentile72000
Maximum88000
Range80500
Interquartile range (IQR)31000

Descriptive statistics

Standard deviation19675.462
Coefficient of variation (CV)0.60953464
Kurtosis-0.32653276
Mean32279.482
Median Absolute Deviation (MAD)13200
Skewness0.78934371
Sum1.62043 × 108
Variance3.8712382 × 108
MonotonicityNot monotonic
2023-09-15T09:48:39.742010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
30000 313
 
6.2%
16000 253
 
5.0%
12600 238
 
4.7%
60000 237
 
4.7%
15000 224
 
4.5%
48000 217
 
4.3%
16800 198
 
3.9%
21000 197
 
3.9%
45000 185
 
3.7%
8400 181
 
3.6%
Other values (34) 2777
55.3%
ValueCountFrequency (%)
7500 80
 
1.6%
8400 181
3.6%
9600 115
2.3%
10000 64
 
1.3%
12000 89
 
1.8%
12600 238
4.7%
12800 107
2.1%
15000 224
4.5%
16000 253
5.0%
16800 198
3.9%
ValueCountFrequency (%)
88000 44
 
0.9%
79200 39
 
0.8%
75000 139
2.8%
72000 58
 
1.2%
70400 52
 
1.0%
70000 74
 
1.5%
61600 47
 
0.9%
60000 237
4.7%
56400 96
1.9%
54000 67
 
1.3%

StoreID
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4898406
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:40.168817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q311
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0285018
Coefficient of variation (CV)0.53786215
Kurtosis-1.2171416
Mean7.4898406
Median Absolute Deviation (MAD)4
Skewness0.0021242975
Sum37599
Variance16.228827
MonotonicityNot monotonic
2023-09-15T09:48:40.583744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
9 370
 
7.4%
13 368
 
7.3%
6 368
 
7.3%
3 367
 
7.3%
2 364
 
7.3%
12 363
 
7.2%
5 362
 
7.2%
10 355
 
7.1%
7 355
 
7.1%
11 355
 
7.1%
Other values (4) 1393
27.7%
ValueCountFrequency (%)
1 354
7.1%
2 364
7.3%
3 367
7.3%
4 350
7.0%
5 362
7.2%
6 368
7.3%
7 355
7.1%
8 343
6.8%
9 370
7.4%
10 355
7.1%
ValueCountFrequency (%)
14 346
6.9%
13 368
7.3%
12 363
7.2%
11 355
7.1%
10 355
7.1%
9 370
7.4%
8 343
6.8%
7 355
7.1%
6 368
7.3%
5 362
7.2%

StoreName
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.4 KiB
Lingga
738 
Sinar Harapan
698 
Buana
368 
Prima Kota
367 
Prima Kelapa Dua
364 
Other values (7)
2485 

Length

Max length16
Median length12
Mean length10.326096
Min length5

Characters and Unicode

Total characters51837
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrestasi Utama
2nd rowPrima Tendean
3rd rowGita Ginara
4th rowGita Ginara
5th rowGita Ginara

Common Values

ValueCountFrequency (%)
Lingga 738
14.7%
Sinar Harapan 698
13.9%
Buana 368
7.3%
Prima Kota 367
7.3%
Prima Kelapa Dua 364
7.3%
Prestasi Utama 363
7.2%
Bonafid 362
7.2%
Harapan Baru 355
7.1%
Buana Indah 355
7.1%
Prima Tendean 354
7.1%
Other values (2) 696
13.9%

Length

2023-09-15T09:48:41.026290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prima 1085
12.6%
harapan 1053
12.3%
lingga 738
 
8.6%
buana 723
 
8.4%
sinar 698
 
8.1%
kota 367
 
4.3%
kelapa 364
 
4.2%
dua 364
 
4.2%
utama 363
 
4.2%
prestasi 363
 
4.2%
Other values (7) 2472
28.8%

Most occurring characters

ValueCountFrequency (%)
a 12842
24.8%
n 5679
11.0%
i 4292
 
8.3%
r 4250
 
8.2%
3570
 
6.9%
g 1822
 
3.5%
P 1794
 
3.5%
m 1448
 
2.8%
t 1443
 
2.8%
u 1442
 
2.8%
Other values (18) 13255
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39677
76.5%
Uppercase Letter 8590
 
16.6%
Space Separator 3570
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12842
32.4%
n 5679
14.3%
i 4292
 
10.8%
r 4250
 
10.7%
g 1822
 
4.6%
m 1448
 
3.6%
t 1443
 
3.6%
u 1442
 
3.6%
e 1435
 
3.6%
p 1417
 
3.6%
Other values (6) 3607
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
P 1794
20.9%
B 1440
16.8%
H 1053
12.3%
L 738
8.6%
K 731
8.5%
G 700
 
8.1%
S 698
 
8.1%
D 364
 
4.2%
U 363
 
4.2%
I 355
 
4.1%
Space Separator
ValueCountFrequency (%)
3570
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48267
93.1%
Common 3570
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12842
26.6%
n 5679
11.8%
i 4292
 
8.9%
r 4250
 
8.8%
g 1822
 
3.8%
P 1794
 
3.7%
m 1448
 
3.0%
t 1443
 
3.0%
u 1442
 
3.0%
B 1440
 
3.0%
Other values (17) 11815
24.5%
Common
ValueCountFrequency (%)
3570
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12842
24.8%
n 5679
11.0%
i 4292
 
8.3%
r 4250
 
8.2%
3570
 
6.9%
g 1822
 
3.5%
P 1794
 
3.5%
m 1448
 
2.8%
t 1443
 
2.8%
u 1442
 
2.8%
Other values (18) 13255
25.6%

GroupStore
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.4 KiB
Prima
1085 
Lingga
738 
Buana
723 
Prestasi
718 
Gita
712 
Other values (2)
1044 

Length

Max length12
Median length8
Mean length6.6143426
Min length4

Characters and Unicode

Total characters33204
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrestasi
2nd rowPrima
3rd rowGita
4th rowGita
5th rowGita

Common Values

ValueCountFrequency (%)
Prima 1085
21.6%
Lingga 738
14.7%
Buana 723
14.4%
Prestasi 718
14.3%
Gita 712
14.2%
Harapan Baru 698
13.9%
Priangan 346
 
6.9%

Length

2023-09-15T09:48:41.460602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-15T09:48:41.860455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
prima 1085
19.0%
lingga 738
12.9%
buana 723
12.6%
prestasi 718
12.6%
gita 712
12.5%
harapan 698
12.2%
baru 698
12.2%
priangan 346
 
6.1%

Most occurring characters

ValueCountFrequency (%)
a 8183
24.6%
i 3599
10.8%
r 3545
10.7%
n 2851
 
8.6%
P 2149
 
6.5%
g 1822
 
5.5%
s 1436
 
4.3%
t 1430
 
4.3%
u 1421
 
4.3%
B 1421
 
4.3%
Other values (7) 5347
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26788
80.7%
Uppercase Letter 5718
 
17.2%
Space Separator 698
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8183
30.5%
i 3599
13.4%
r 3545
13.2%
n 2851
 
10.6%
g 1822
 
6.8%
s 1436
 
5.4%
t 1430
 
5.3%
u 1421
 
5.3%
m 1085
 
4.1%
e 718
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
P 2149
37.6%
B 1421
24.9%
L 738
 
12.9%
G 712
 
12.5%
H 698
 
12.2%
Space Separator
ValueCountFrequency (%)
698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32506
97.9%
Common 698
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8183
25.2%
i 3599
11.1%
r 3545
10.9%
n 2851
 
8.8%
P 2149
 
6.6%
g 1822
 
5.6%
s 1436
 
4.4%
t 1430
 
4.4%
u 1421
 
4.4%
B 1421
 
4.4%
Other values (6) 4649
14.3%
Common
ValueCountFrequency (%)
698
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8183
24.6%
i 3599
10.8%
r 3545
10.7%
n 2851
 
8.6%
P 2149
 
6.5%
g 1822
 
5.5%
s 1436
 
4.3%
t 1430
 
4.3%
u 1421
 
4.3%
B 1421
 
4.3%
Other values (7) 5347
16.1%

Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.4 KiB
General Trade
2851 
Modern Trade
2169 

Length

Max length13
Median length13
Mean length12.567928
Min length12

Characters and Unicode

Total characters63091
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGeneral Trade
2nd rowModern Trade
3rd rowGeneral Trade
4th rowGeneral Trade
5th rowGeneral Trade

Common Values

ValueCountFrequency (%)
General Trade 2851
56.8%
Modern Trade 2169
43.2%

Length

2023-09-15T09:48:42.331233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-15T09:48:42.665084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
trade 5020
50.0%
general 2851
28.4%
modern 2169
21.6%

Most occurring characters

ValueCountFrequency (%)
e 12891
20.4%
r 10040
15.9%
a 7871
12.5%
d 7189
11.4%
n 5020
 
8.0%
5020
 
8.0%
T 5020
 
8.0%
G 2851
 
4.5%
l 2851
 
4.5%
M 2169
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48031
76.1%
Uppercase Letter 10040
 
15.9%
Space Separator 5020
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12891
26.8%
r 10040
20.9%
a 7871
16.4%
d 7189
15.0%
n 5020
 
10.5%
l 2851
 
5.9%
o 2169
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
T 5020
50.0%
G 2851
28.4%
M 2169
21.6%
Space Separator
ValueCountFrequency (%)
5020
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58071
92.0%
Common 5020
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12891
22.2%
r 10040
17.3%
a 7871
13.6%
d 7189
12.4%
n 5020
 
8.6%
T 5020
 
8.6%
G 2851
 
4.9%
l 2851
 
4.9%
M 2169
 
3.7%
o 2169
 
3.7%
Common
ValueCountFrequency (%)
5020
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12891
20.4%
r 10040
15.9%
a 7871
12.5%
d 7189
11.4%
n 5020
 
8.0%
5020
 
8.0%
T 5020
 
8.0%
G 2851
 
4.5%
l 2851
 
4.5%
M 2169
 
3.4%

Latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.9422362
Minimum-7.797068
Maximum5.54829
Zeros0
Zeros (%)0.0%
Negative3612
Negative (%)72.0%
Memory size78.4 KiB
2023-09-15T09:48:42.995432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7.797068
5-th percentile-7.797068
Q1-6.914864
median-5.135399
Q30.533505
95-th percentile5.54829
Maximum5.54829
Range13.345358
Interquartile range (IQR)7.448369

Descriptive statistics

Standard deviation4.323225
Coefficient of variation (CV)-1.4693671
Kurtosis-0.94353879
Mean-2.9422362
Median Absolute Deviation (MAD)2.115046
Skewness0.67736993
Sum-14770.026
Variance18.690274
MonotonicityNot monotonic
2023-09-15T09:48:43.414923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
-3.654703 370
 
7.4%
-1.26916 368
 
7.3%
-5.135399 368
 
7.3%
-7.797068 367
 
7.3%
-6.914864 364
 
7.3%
-2.990934 363
 
7.2%
-7.250445 362
 
7.2%
3.597031 355
 
7.1%
3.316694 355
 
7.1%
0.533505 355
 
7.1%
Other values (4) 1393
27.7%
ValueCountFrequency (%)
-7.797068 367
7.3%
-7.250445 362
7.2%
-6.966667 350
7.0%
-6.914864 364
7.3%
-6.2 354
7.1%
-5.45 346
6.9%
-5.135399 368
7.3%
-3.654703 370
7.4%
-2.990934 363
7.2%
-1.26916 368
7.3%
ValueCountFrequency (%)
5.54829 343
6.8%
3.597031 355
7.1%
3.316694 355
7.1%
0.533505 355
7.1%
-1.26916 368
7.3%
-2.990934 363
7.2%
-3.654703 370
7.4%
-5.135399 368
7.3%
-5.45 346
6.9%
-6.2 354
7.1%

Longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.60079
Minimum95.323753
Maximum128.19064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:43.767246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95.323753
5-th percentile95.323753
Q1104.75655
median110.37053
Q3114.59011
95-th percentile128.19064
Maximum128.19064
Range32.86689
Interquartile range (IQR)9.833557

Descriptive statistics

Standard deviation8.3575928
Coefficient of variation (CV)0.07625486
Kurtosis-0.1358849
Mean109.60079
Median Absolute Deviation (MAD)5.613975
Skewness0.4054297
Sum550195.96
Variance69.849357
MonotonicityNot monotonic
2023-09-15T09:48:44.155827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
128.190643 370
 
7.4%
116.825264 368
 
7.3%
119.42379 368
 
7.3%
110.370529 367
 
7.3%
107.608238 364
 
7.3%
104.756554 363
 
7.2%
112.768845 362
 
7.2%
98.678513 355
 
7.1%
114.590111 355
 
7.1%
101.447403 355
 
7.1%
Other values (4) 1393
27.7%
ValueCountFrequency (%)
95.323753 343
6.8%
98.678513 355
7.1%
101.447403 355
7.1%
104.756554 363
7.2%
105.26667 346
6.9%
106.816666 354
7.1%
107.608238 364
7.3%
110.370529 367
7.3%
110.416664 350
7.0%
112.768845 362
7.2%
ValueCountFrequency (%)
128.190643 370
7.4%
119.42379 368
7.3%
116.825264 368
7.3%
114.590111 355
7.1%
112.768845 362
7.2%
110.416664 350
7.0%
110.370529 367
7.3%
107.608238 364
7.3%
106.816666 354
7.1%
105.26667 346
6.9%

Product Name
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.4 KiB
Thai Tea
814 
Cheese Stick
620 
Ginger Candy
530 
Coffee Candy
522 
Crackers
519 
Other values (5)
2015 

Length

Max length13
Median length11
Mean length9.0681275
Min length3

Characters and Unicode

Total characters45522
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCrackers
2nd rowYoghurt
3rd rowChoco Bar
4th rowChoco Bar
5th rowYoghurt

Common Values

ValueCountFrequency (%)
Thai Tea 814
16.2%
Cheese Stick 620
12.4%
Ginger Candy 530
10.6%
Coffee Candy 522
10.4%
Crackers 519
10.3%
Yoghurt 488
9.7%
Oat 485
9.7%
Choco Bar 397
7.9%
Potato Chip 390
7.8%
Cashew 255
 
5.1%

Length

2023-09-15T09:48:44.601016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-15T09:48:45.018238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
candy 1052
12.7%
thai 814
9.8%
tea 814
9.8%
cheese 620
 
7.5%
stick 620
 
7.5%
ginger 530
 
6.4%
coffee 522
 
6.3%
crackers 519
 
6.3%
yoghurt 488
 
5.9%
oat 485
 
5.8%
Other values (5) 1829
22.1%

Most occurring characters

ValueCountFrequency (%)
e 5022
 
11.0%
a 4726
 
10.4%
3803
 
8.4%
C 3755
 
8.2%
h 2964
 
6.5%
o 2584
 
5.7%
r 2453
 
5.4%
t 2373
 
5.2%
i 2354
 
5.2%
T 1628
 
3.6%
Other values (17) 13860
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33426
73.4%
Uppercase Letter 8293
 
18.2%
Space Separator 3803
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5022
15.0%
a 4726
14.1%
h 2964
8.9%
o 2584
7.7%
r 2453
 
7.3%
t 2373
 
7.1%
i 2354
 
7.0%
n 1582
 
4.7%
c 1536
 
4.6%
s 1394
 
4.2%
Other values (8) 6438
19.3%
Uppercase Letter
ValueCountFrequency (%)
C 3755
45.3%
T 1628
19.6%
S 620
 
7.5%
G 530
 
6.4%
Y 488
 
5.9%
O 485
 
5.8%
B 397
 
4.8%
P 390
 
4.7%
Space Separator
ValueCountFrequency (%)
3803
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41719
91.6%
Common 3803
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5022
12.0%
a 4726
 
11.3%
C 3755
 
9.0%
h 2964
 
7.1%
o 2584
 
6.2%
r 2453
 
5.9%
t 2373
 
5.7%
i 2354
 
5.6%
T 1628
 
3.9%
n 1582
 
3.8%
Other values (16) 12278
29.4%
Common
ValueCountFrequency (%)
3803
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5022
 
11.0%
a 4726
 
10.4%
3803
 
8.4%
C 3755
 
8.2%
h 2964
 
6.5%
o 2584
 
5.7%
r 2453
 
5.4%
t 2373
 
5.2%
i 2354
 
5.2%
T 1628
 
3.6%
Other values (17) 13860
30.4%

Price_y
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9684.8008
Minimum3200
Maximum18000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:45.450609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3200
5-th percentile3200
Q14200
median9400
Q315000
95-th percentile18000
Maximum18000
Range14800
Interquartile range (IQR)10800

Descriptive statistics

Standard deviation4600.7088
Coefficient of variation (CV)0.47504423
Kurtosis-1.1395182
Mean9684.8008
Median Absolute Deviation (MAD)5200
Skewness0.16819672
Sum48617700
Variance21166521
MonotonicityNot monotonic
2023-09-15T09:48:45.846709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4200 814
16.2%
15000 620
12.4%
3200 530
10.6%
9400 522
10.4%
7500 519
10.3%
10000 488
9.7%
16000 485
9.7%
8800 397
7.9%
12000 390
7.8%
18000 255
 
5.1%
ValueCountFrequency (%)
3200 530
10.6%
4200 814
16.2%
7500 519
10.3%
8800 397
7.9%
9400 522
10.4%
10000 488
9.7%
12000 390
7.8%
15000 620
12.4%
16000 485
9.7%
18000 255
 
5.1%
ValueCountFrequency (%)
18000 255
 
5.1%
16000 485
9.7%
15000 620
12.4%
12000 390
7.8%
10000 488
9.7%
9400 522
10.4%
8800 397
7.9%
7500 519
10.3%
4200 814
16.2%
3200 530
10.6%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.003586
Minimum0
Maximum72
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:46.268272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q130
median39
Q351
95-th percentile60
Maximum72
Range72
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.834719
Coefficient of variation (CV)0.32083922
Kurtosis-0.65678737
Mean40.003586
Median Absolute Deviation (MAD)10
Skewness0.024523613
Sum200818
Variance164.73001
MonotonicityNot monotonic
2023-09-15T09:48:46.756491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 207
 
4.1%
34 187
 
3.7%
31 166
 
3.3%
60 166
 
3.3%
26 165
 
3.3%
33 160
 
3.2%
40 155
 
3.1%
37 149
 
3.0%
51 147
 
2.9%
54 143
 
2.8%
Other values (44) 3375
67.2%
ValueCountFrequency (%)
0 9
 
0.2%
2 16
 
0.3%
3 6
 
0.1%
18 66
1.3%
19 104
2.1%
20 48
1.0%
21 73
1.5%
22 108
2.2%
23 83
1.7%
24 83
1.7%
ValueCountFrequency (%)
72 10
 
0.2%
70 8
 
0.2%
69 12
 
0.2%
68 12
 
0.2%
66 13
 
0.3%
65 13
 
0.3%
62 50
 
1.0%
61 93
1.9%
60 166
3.3%
59 122
2.4%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.4 KiB
0
2746 
1
2274 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5020
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2746
54.7%
1 2274
45.3%

Length

2023-09-15T09:48:47.219426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-15T09:48:47.551042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2746
54.7%
1 2274
45.3%

Most occurring characters

ValueCountFrequency (%)
0 2746
54.7%
1 2274
45.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5020
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2746
54.7%
1 2274
45.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2746
54.7%
1 2274
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2746
54.7%
1 2274
45.3%

Marital Status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.4 KiB
Married
3823 
Single
1197 

Length

Max length7
Median length7
Mean length6.7615538
Min length6

Characters and Unicode

Total characters33943
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 3823
76.2%
Single 1197
 
23.8%

Length

2023-09-15T09:48:47.855325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-15T09:48:48.058844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
married 3823
76.2%
single 1197
 
23.8%

Most occurring characters

ValueCountFrequency (%)
r 7646
22.5%
i 5020
14.8%
e 5020
14.8%
M 3823
11.3%
a 3823
11.3%
d 3823
11.3%
S 1197
 
3.5%
n 1197
 
3.5%
g 1197
 
3.5%
l 1197
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28923
85.2%
Uppercase Letter 5020
 
14.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 7646
26.4%
i 5020
17.4%
e 5020
17.4%
a 3823
13.2%
d 3823
13.2%
n 1197
 
4.1%
g 1197
 
4.1%
l 1197
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
M 3823
76.2%
S 1197
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 33943
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 7646
22.5%
i 5020
14.8%
e 5020
14.8%
M 3823
11.3%
a 3823
11.3%
d 3823
11.3%
S 1197
 
3.5%
n 1197
 
3.5%
g 1197
 
3.5%
l 1197
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 7646
22.5%
i 5020
14.8%
e 5020
14.8%
M 3823
11.3%
a 3823
11.3%
d 3823
11.3%
S 1197
 
3.5%
n 1197
 
3.5%
g 1197
 
3.5%
l 1197
 
3.5%

Income
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct369
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6237131
Minimum0
Maximum71.3
Zeros185
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size78.4 KiB
2023-09-15T09:48:48.297291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14.22
median7.72
Q310.78
95-th percentile18.89
Maximum71.3
Range71.3
Interquartile range (IQR)6.56

Descriptive statistics

Standard deviation6.5182417
Coefficient of variation (CV)0.75585094
Kurtosis19.876027
Mean8.6237131
Median Absolute Deviation (MAD)3.285
Skewness2.9748029
Sum43291.04
Variance42.487474
MonotonicityNot monotonic
2023-09-15T09:48:48.639390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 185
 
3.7%
5.12 44
 
0.9%
8.96 42
 
0.8%
6.05 42
 
0.8%
9.57 42
 
0.8%
5.35 35
 
0.7%
3.28 33
 
0.7%
6.19 33
 
0.7%
9.68 32
 
0.6%
2.69 31
 
0.6%
Other values (359) 4501
89.7%
ValueCountFrequency (%)
0 185
3.7%
0.06 10
 
0.2%
0.14 13
 
0.3%
0.18 12
 
0.2%
0.57 10
 
0.2%
0.74 9
 
0.2%
0.98 6
 
0.1%
1 7
 
0.1%
1.12 12
 
0.2%
1.28 16
 
0.3%
ValueCountFrequency (%)
71.3 8
0.2%
54.2 14
0.3%
35.78 13
0.3%
33.77 12
0.2%
28.23 13
0.3%
25.22 10
0.2%
23.84 11
0.2%
21.81 16
0.3%
20.81 17
0.3%
20.64 9
0.2%

Interactions

2023-09-15T09:48:28.332611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:01.495973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:04.825750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:07.949094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:11.290890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:14.568519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:17.678957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:20.807689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:23.534433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:25.892198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:28.625632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:01.992914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:05.155199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:08.247829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:11.623555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:14.866218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:17.969740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:21.002100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:23.824582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:26.082517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:28.943549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:02.305860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:05.483487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:08.567992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:11.971494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:15.192583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:18.285613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:21.487960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:24.047871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:26.312701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:29.222252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:02.586326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:05.794830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:08.855533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:12.299290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:15.478507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:18.587986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:21.692953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:24.242158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:26.503196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:29.531415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:02.895672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:06.122671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:09.171571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:12.638590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:15.806872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:18.916777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:21.940549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:24.470177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:26.762666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:29.834364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:03.203517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:06.429505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:09.472556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:12.958394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:16.114252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:19.229325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:22.153129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:24.687382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:26.986479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:30.142855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:03.506869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:06.743083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:09.772217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:13.280165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:16.427727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:19.550116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:22.378040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:24.931468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:27.205772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:30.433605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:03.811023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:07.059184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:10.069962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:13.587047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:16.730165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:19.848973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:22.590721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:25.158541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:27.416076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:30.739212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:04.141718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:07.371057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:10.627381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:13.903328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:17.037888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:20.170141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:22.889293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:25.383783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:27.725034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:31.055677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:04.480851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:07.616050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:10.942295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:14.237391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:17.350915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:20.488376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:23.213874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:25.607059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-15T09:48:28.028491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-15T09:48:48.906636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CustomerIDPrice_xQtyTotalAmountStoreIDLatitudeLongitudePrice_yAgeIncomeProductIDStoreNameGroupStoreTypeProduct NameGenderMarital Status
CustomerID1.000-0.019-0.006-0.0290.0040.008-0.006-0.019-0.034-0.0210.0000.0130.0000.0170.0000.1630.182
Price_x-0.0191.000-0.3900.467-0.028-0.0240.0181.0000.016-0.0021.0000.0270.0260.0151.0000.0410.000
Qty-0.006-0.3901.0000.5860.0110.002-0.010-0.390-0.032-0.0250.2890.0270.0200.0270.2890.0180.020
TotalAmount-0.0290.4670.5861.000-0.012-0.0140.0070.467-0.020-0.0230.3690.0220.0180.0460.3690.0280.000
StoreID0.004-0.0280.011-0.0121.0000.594-0.172-0.028-0.005-0.0030.0180.9120.8390.7470.0180.0170.000
Latitude0.008-0.0240.002-0.0140.5941.000-0.342-0.0240.0090.0150.0000.8730.6990.7470.0000.0000.000
Longitude-0.0060.018-0.0100.007-0.172-0.3421.0000.0180.013-0.0030.0090.8830.7840.9240.0090.0230.000
Price_y-0.0191.000-0.3900.467-0.028-0.0240.0181.0000.016-0.0021.0000.0270.0260.0151.0000.0410.000
Age-0.0340.016-0.032-0.020-0.0050.0090.0130.0161.0000.6130.0030.0000.0000.0000.0030.1880.638
Income-0.021-0.002-0.025-0.023-0.0030.015-0.003-0.0020.6131.0000.0120.0200.0000.0000.0120.1420.410
ProductID0.0001.0000.2890.3690.0180.0000.0091.0000.0030.0121.0000.0210.0230.0001.0000.0670.000
StoreName0.0130.0270.0270.0220.9120.8730.8830.0270.0000.0200.0211.0000.9570.9990.0210.0000.000
GroupStore0.0000.0260.0200.0180.8390.6990.7840.0260.0000.0000.0230.9571.0001.0000.0230.0000.000
Type0.0170.0150.0270.0460.7470.7470.9240.0150.0000.0000.0000.9991.0001.0000.0000.0000.000
Product Name0.0001.0000.2890.3690.0180.0000.0091.0000.0030.0121.0000.0210.0230.0001.0000.0670.000
Gender0.1630.0410.0180.0280.0170.0000.0230.0410.1880.1420.0670.0000.0000.0000.0671.0000.018
Marital Status0.1820.0000.0200.0000.0000.0000.0000.0000.6380.4100.0000.0000.0000.0000.0000.0181.000

Missing values

2023-09-15T09:48:31.537286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-15T09:48:32.644423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TransactionIDCustomerIDDateProductIDPrice_xQtyTotalAmountStoreIDStoreNameGroupStoreTypeLatitudeLongitudeProduct NamePrice_yAgeGenderMarital StatusIncome
0TR1136932801/01/2022P3750043000012Prestasi UtamaPrestasiGeneral Trade-2.990934104.756554Crackers7500360Married10.53
1TR1635616501/01/2022P9100007700001Prima TendeanPrimaModern Trade-6.200000106.816666Yoghurt10000441Married14.58
2TR198418301/01/2022P188004352004Gita GinaraGitaGeneral Trade-6.966667110.416664Choco Bar8800271Single0.18
3TR3525616001/01/2022P188007616004Gita GinaraGitaGeneral Trade-6.966667110.416664Choco Bar8800481Married12.57
4TR4123138601/01/2022P9100001100004Gita GinaraGitaGeneral Trade-6.966667110.416664Yoghurt10000330Married6.95
5TR5167528301/01/2022P10150001150005BonafidGitaGeneral Trade-7.250445112.768845Cheese Stick15000191Single0.00
6TR542875101/01/2022P8160002320002Prima Kelapa DuaPrimaModern Trade-6.914864107.608238Oat16000360Married7.95
7TR674554901/01/2022P5420031260013BuanaBuanaGeneral Trade-1.269160116.825264Thai Tea4200441Married13.48
8TR7304122201/01/2022P9100006600004Gita GinaraGitaGeneral Trade-6.966667110.416664Yoghurt10000450Married15.03
9TR759627001/01/2022P7940021880014PrianganPrianganModern Trade-5.450000105.266670Coffee Candy9400491Married8.81
TransactionIDCustomerIDDateProductIDPrice_xQtyTotalAmountStoreIDStoreNameGroupStoreTypeLatitudeLongitudeProduct NamePrice_yAgeGenderMarital StatusIncome
5010TR138026631/12/2022P91000033000011Sinar HarapanPrestasiGeneral Trade0.533505101.447403Yoghurt10000721Married4.72
5011TR3157421231/12/2022P7940021880013BuanaBuanaGeneral Trade-1.269160116.825264Coffee Candy9400360Married7.96
5012TR3754439531/12/2022P375002150009LinggaLinggaModern Trade-3.654703128.190643Crackers7500280Married3.39
5013TR3812925331/12/2022P375005375004Gita GinaraGitaGeneral Trade-6.966667110.416664Crackers7500370Married4.32
5014TR4589923231/12/2022P6180001180009LinggaLinggaModern Trade-3.654703128.190643Cashew18000620Married7.32
5015TR5442324331/12/2022P10150005750003Prima KotaPrimaModern Trade-7.797068110.370529Cheese Stick15000380Married3.34
5016TR560427131/12/2022P232004128009LinggaLinggaModern Trade-3.654703128.190643Ginger Candy3200290Married4.74
5017TR812245231/12/2022P794006564009LinggaLinggaModern Trade-3.654703128.190643Coffee Candy9400370Married3.73
5018TR850161831/12/2022P81600034800013BuanaBuanaGeneral Trade-1.269160116.825264Oat16000470Married13.60
5019TR856845531/12/2022P8160001160006LinggaLinggaModern Trade-5.135399119.423790Oat16000341Married8.44